针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search...针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法形成窗口维度和缓冲区维度的特征向量,通过两种维度的模板匹配实现用电设备的运行状态匹配和状态切换时刻定位。基于家用电冰箱的仿真实验结果表明,所提方法具有检测速度快、准确率高等优点,可为用电设备状态监测领域提供参考。展开更多
The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correct...The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.展开更多
为了解决传统自适应阈值算法对时间序列方差跟踪能力不足,以及故障阶段带宽自动放大的问题,提出了紧广义自回归条件异方差(Compact General Auto-Regressive Conditional Heteroskedasticity,CGARCH)模型。针对液体火箭发动机稳态试车...为了解决传统自适应阈值算法对时间序列方差跟踪能力不足,以及故障阶段带宽自动放大的问题,提出了紧广义自回归条件异方差(Compact General Auto-Regressive Conditional Heteroskedasticity,CGARCH)模型。针对液体火箭发动机稳态试车数据的波动性特点,提出一种基于自回归(Auto-Regressive,AR)模型和CGARCH模型的自适应阈值故障检测算法。采用AR模型对稳态参数的均值进行估计,并采用CGARCH模型对稳态参数的方差进行估计,从而利用均值和方差的估计值自适应地构造检测阈值。用某氢氧火箭发动机的热试车数据进行验证,结果表明,该算法能够准确、快速、灵敏地检测液体火箭发动机故障,在正常工作阶段,能够有效跟踪数据波动性,在故障阶段,能够避免阈值变宽带来的漏检。展开更多
文摘针对现阶段用电设备状态监测技术存在的处理速度较慢、准确率较低等问题,文中基于多突变点检测和模板匹配策略提出了一种用电设备在线状态监测方法。该方法在缓冲区模型和滑动窗口模型的基础上,利用多路搜索树突变点检测(Ternary Search Tree and Kolmogorov-Smirnov,TSTKS)算法形成窗口维度和缓冲区维度的特征向量,通过两种维度的模板匹配实现用电设备的运行状态匹配和状态切换时刻定位。基于家用电冰箱的仿真实验结果表明,所提方法具有检测速度快、准确率高等优点,可为用电设备状态监测领域提供参考。
基金This project was supported by the National Natural Science Foundation (No. 79800012 and No. 79400014).
文摘The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.
文摘为了解决传统自适应阈值算法对时间序列方差跟踪能力不足,以及故障阶段带宽自动放大的问题,提出了紧广义自回归条件异方差(Compact General Auto-Regressive Conditional Heteroskedasticity,CGARCH)模型。针对液体火箭发动机稳态试车数据的波动性特点,提出一种基于自回归(Auto-Regressive,AR)模型和CGARCH模型的自适应阈值故障检测算法。采用AR模型对稳态参数的均值进行估计,并采用CGARCH模型对稳态参数的方差进行估计,从而利用均值和方差的估计值自适应地构造检测阈值。用某氢氧火箭发动机的热试车数据进行验证,结果表明,该算法能够准确、快速、灵敏地检测液体火箭发动机故障,在正常工作阶段,能够有效跟踪数据波动性,在故障阶段,能够避免阈值变宽带来的漏检。
